Objective To understand the mortality and potential life loss due to coronary heart disease (CHD) and stroke in Wujiang District, Suzhou from 2011 to 2022, and to provide strategies and basis for the prevention and treatment of CHD and stroke. Methods We collected the data of death cases due to CHD and stroke from the death monitoring system in Suzhou from 2011 to 2022. The mortality of CHD and stroke, potential years of life lost (potential years of life lost , PYLL), average years of life lost (average years of life lost , AYLL) and potential years of life lost rate (potential years of life lost rate , PYLLR) were calculated to analyze the development trend of death and disease burden of CHD and stroke. Results From 2011 to 2022, the crude mortality of CHD was 31.91/10 million, and that of stroke was 118.93/10 million. CHD and stroke mortality rates both showed an upward trend（P＜0.05, a statistically significant trend）. From 2011 to 2022, the mortality rate of CHD and stroke in Wujiang District increased rapidly with the increase of age. From 2011 to 2022, the disease burden caused by CHD totaled 11005 person-years, with PYLLR of 1.26% and AYLL of 12.34 years per person. The PYLL caused by stroke was 13 587.5 people-years, the PYLLR was 1.55%, and the AYLL was 8.93 years per person. PYLL, PYLLR and AYLL all decreased in women（P<0.05）, with no significant change in men（P>0.05）. Conclusion From 2011 to 2022, the mortality rate of CHD and stroke in Wujiang District appeared a tendency towards a rise, effective intervention and prevention measures should be taken among elderly and male residents.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

BACKGROUND: LY01005 (Goserelin acetate sustained-release microsphere injection) is a modified gonadotropin-releasing hormone (GnRH) agonist injected monthly. This phase III trial study aimed to evaluated the efficacy and safety of LY01005 in Chinese patients with prostate cancer. METHODS: We conducted a randomized controlled, open-label, non-inferiority trial across 49 sites in China. This study included 290 patients with prostate cancer who received either LY01005 or goserelin implants every 28 days for three injections. The primary efficacy endpoints were the percentage of patients with testosterone suppression ≤50 ng/dL at day 29 and the cumulative probability of testosterone ≤50 ng/dL from day 29 to 85. Non-inferiority was prespecified at a margin of -10%. Secondary endpoints included significant castration (≤20 ng/dL), testosterone surge within 72 h following repeated dosing, and changes in luteinizing hormone, follicle-stimulating hormone, and prostate specific antigen levels. RESULTS: On day 29, in the LY01005 and goserelin implant groups, testosterone concentrations fell below medical-castration levels in 99.3% (142/143) and 100% (140/140) of patients, respectively, with a difference of -0.7% (95% confidence interval [CI], -3.9% to 2.0%) between the two groups. The cumulative probabilities of maintaining castration from days 29 to 85 were 99.3% and 97.8%, respectively, with a between-group difference of 1.5% (95% CI, -1.3% to 4.4%). Both results met the criterion for non-inferiority. Secondary endpoints were similar between groups. Both treatments were well-tolerated. LY01005 was associated with fewer injection-site reactions than the goserelin implant (0% vs . 1.4% [2/145]). CONCLUSION: LY01005 is as effective as goserelin implants in reducing testosterone to castration levels, with a similar safety profile. TRIAL REGISTRATION ClinicalTrials.gov, NCT04563936.
Humans ; Male ; Antineoplastic Agents, Hormonal/therapeutic use* ; East Asian People ; Gonadotropin-Releasing Hormone/agonists* ; Goserelin/therapeutic use* ; Prostate-Specific Antigen ; Prostatic Neoplasms/drug therapy* ; Testosterone

Type 2 diabetes (T2D) is often accompanied with an induction of retinaldehyde dehydrogenase 1 (RALDH1 or ALDH1A1) expression and a consequent decrease in hepatic retinaldehyde (Rald) levels. However, the role of hepatic Rald deficiency in T2D progression remains unclear. In this study, we demonstrated that reversing T2D-mediated hepatic Rald deficiency by Rald or citral treatments, or liver-specific Raldh1 silencing substantially lowered fasting glycemia levels, inhibited hepatic glucogenesis, and downregulated phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase (G6PC) expression in diabetic db/db mice. Fasting glycemia and Pck1/G6pc mRNA expression levels were strongly negatively correlated with hepatic Rald levels, indicating the involvement of hepatic Rald depletion in T2D deterioration. A similar result that liver-specific Raldh1 silencing improved glucose metabolism was also observed in high-fat diet-fed mice. In primary human hepatocytes and oleic acid-treated HepG2 cells, Rald or Rald + RALDH1 silencing resulted in decreased glucose production and downregulated PCK1/G6PC mRNA and protein expression. Mechanistically, Rald downregulated direct repeat 1-mediated PCK1 and G6PC expression by antagonizing retinoid X receptor α, as confirmed by luciferase reporter assays and molecular docking. These results highlight the link between hepatic Rald deficiency, glucose dyshomeostasis, and the progression of T2D, whilst also suggesting RALDH1 as a potential therapeutic target for T2D.

To develop a rapid and sensitive liquid chromatography-tandem mass spectrometry(LC-MS/MS)method for the determination of endogenous glutathione in rat plasma. Glycyltyrosine was used as the internal standard(IS)and 4-(N-maleimido)phenyl trimethylammonium iodide(MPTA)was used as the derivation reagent. Chromatographic separation was achieved on a Zorbax HILIC PLUS column(4. 6 mm×100 mm, 3. 5 μm)and the mobile phase consisted of acetonitrile and 0. 1% formic acid(75 ∶25)pumped at a flow rate of 1. 0 mL/min. Detection was carried out on a triple quadrupole tandem mass spectrometer by selected reaction monitoring(SRM)in the can meet positive ion mode. The linearity ranged from 3. 000 to 2 000 ng/mL(r=0. 997 1); and the limit of detection of glutathione in rat plasma was 10 pmol/L. Matrix effect, stability, precision and accuracy of the method met the requirements. The proposed method was proved to be selective and sensitive, which is suitable for the quantification of endogenous glutathione in rat plasma.

Objective To compare the effectiveness,correlativity,and acceptability of the three methods:bone marrow smear,biopsy and flow cytometry in diagnosis of lymphoma patients with bone marrow involvement.Methods 68 cases of early stage lymphoma were studied by observing and comparing positive rates of the three methods:bone marrow smear,biopsy and flow cytometry.Results After confirming the bone marrow involvement,the positive rates for the 68 cases using bone marrow smear,biopsy and flow cytometry were 16.2 ％ (11/68),33.8 ％ (23/68) and 10.3 ％ (7/68) respectively.Bone marrow biopsy had the highest positive rate compared to the other methods.It was statistically significant when comparing the differences of the 3 methods (P ＜ 0.05).According to the correlativity analysis of the 3 methods,bone marrow smear and biopsy correlated with each other (P =0.002),while flow cytometery did not (P =0.270).Conclusions Morphological examination of the bone marrow smear is a fundamental method to test bone marrow involvement in lymphoma.Bone marrow biopsy creates the highest positive rate and has clear advantage compared to the other methods,however,bone marrow smear must be used at the same time as a complement.

BACKGROUND AND OBJECTIVEThe aim of this study is to determine the short-term efficacy, toxicity and the rate of life-quality improvement of BSD2000 deep hyperthermia combined with chemotherapy of PT regimen in patients with non-small cell lung cancer (NSCLC) by comparation with PT regimen alone.

METHODSSixty patients with NSCLC were randomly divided into the treatment group and control group, with 30 each. The treatment group was treated with chemotherapy (paclitaxel: 135 mg/m2 ivdirp 3 h qd d1+cisplatin: 20 mg/m2 ivdirp qd d1-5) in combination with BSD2000 deep hyperthermia, and hyperthermia was positioned precisely and maintained for 60 min (2 times a cycle: d1, 4 after the end of chemotherapy within two hours). The control group was treated with chemotherapy alone. Treatment response in both groups were evaluated as well as side-effects after 3 cycles. By observing the results, comparing response rate, toxic side effects and quality of life improvement rate in two groups.

RESULTSThe efficiency and the rate of life-quality improvement in the treatment group were 63.33%, 76.67% respectively, and 36.67%, 40.00% in the control group respectively. There were significant differences between two groups (P < 0.05). The main side-effects were myelosuppression and gastrointestinal reactions, no significant difference between two groups (P > 0.05).

CONCLUSIONBSD2000 deep hyperthermia combined with chemotherapy in patients with NSCLC can significantly increase the efficacy, response rate and quality of life improvement and without increasing side-effects compared to chemotherapy alone.

Aged ; Carcinoma, Non-Small-Cell Lung ; drug therapy ; therapy ; Female ; Humans ; Hyperthermia, Induced ; Lung Neoplasms ; drug therapy ; therapy ; Male ; Paclitaxel ; therapeutic use ; Treatment Outcome